Comprehensive review of vision-based fall detection systems

Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers

Automatic Fall Risk Detection based on Imbalanced Data

In recent years, the declining birthrate and aging population have gradually brought countries into an ageing society. Regarding accidents that occur amongst the elderly, falls are an essential problem that quickly causes indirect physical loss. In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks. We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates. Since fall data is rare in real-world situations, we train and evaluate our approach in a highly imbalanced data setting. We assess not only different imbalanced data handling methods but also different machine learning algorithms. After oversampling on our training data, the K-Nearest Neighbors (KNN) algorithm achieves the best performance. The F1 scores for three different classes, Normal, Fall, and Lying, are 1.00, 0.85 and 0.96, which is comparable to previous research. The experiment shows that our approach is more interpretable with the key feature from skeleton information. Moreover, it can apply in multi-people scenarios and has robustness on medium occlusion

Design and development of a telerehabilitation app

Background Neuromusculoskeletal injuries are a common condition and after medication and surgery, most patients still suffer from physical deficits and even psychological disorders due to the lack of scientific and effective rehabilitation. However, the high cost of rehabilitation makes it impossible for many patients to receive a complete and scientific post-operative rehabilitation, so self-training at home can provide the opportunity for most patients to receive good treatment. Objective In order to enable patients to rehabilitate themselves at home, a new solution is proposed for telemedicine, where patients can play a serious game of simple rehabilitation with just an ordinary computer with a camera. Apart from the development phase, no further involvement of occupational therapists is required, significantly reducing the cost and complexity of rehabilitation. Methods This study develops a simple serious game for playing the piano based on OpenCV, MediaPipe and Unity. The game is developed with the user in mind, recording game data to facilitate the analysis and processing of user data and recording each user's progress, making the game much more beneficial and applicable. Results This study saves all results on the web, allows simple rehabilitation using only a computer, records patient progress saves observed behavioural information and expressions, receives positive feedback during user use and inferred from the study that this is indeed a viable solution. Conclusion The combined results suggest that serious game as a rehabilitation treatment is a viable solution, not only as an excellent rehabilitation solution for those who are financially disadvantaged, but also as a good complementary training for those who can afford expensive rehabilitation treatment. Overall, Serious Play can prove beneficial to rehabilitation by providing measurable data on each session and quantifying the patient's training

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 125

This special bibliography lists 323 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1974

Estimating the trauma-death interval : a histological investigation of fracture healing.

The accurate, reliable estimation of the ‘age’ of a fracture, or the time elapsed since trauma was sustained, has important implications In a variety of forensic contexts. Such information could greatly aid the forensic diagnosis of child abuse, the reconstruction of events during a violent incident such as homicide or a road traffic accident, and assist in the identification of unknown remains. Forensic fracture dating has largely relied on radiographical and histological evidence, but has lacked precision and consistency. The research presented here alms to test the hypothesis that correlations exist between the histologically- and immunohistochemically-observable phenomena at a fracture site and the known trauma-death interval of an individual. This was achieved by comparing the known trauma-death interval (TDI) to the extent of healing visible on histological slides prepared from formalin-fixed, paraffin-embedded, decalcified blocks of bone excised from the fracture site of 52 rib, skull and femur fractures from 29 individual forensic cases submitted to the Medico-Legal Centre Sheffield between 1992 and 2002. The slides were stained with haematoxylin and eosin to stain nuclei and cytoplasm, Peris’ Prussian Blue stain for haemosiderin granules, mono-clonal anti-CD68 antibody for osteoclasts, and anti-bone sialoprotein antibody as an osteoblast and osteocyte marker. Quantifiable parameters such as the percentage cover of red blood cells, of living and necrotic compact bone, and the size, abundance and dispersal of immuno-positive and inflammatory cells were examined and compared to the TDI using human observers and Scion Image histomorphometry software. Statistically significant correlations were found between TDI and the presence of haemosiderin granules later than three days post-trauma; and the dispersal and location of CD68 positive cells; as well as the estimated percentage cover of fibroblasts and red blood cells at the fracture site. Other trends and correlations were found, which contribute to the understanding of bone’s immediate responses to trauma. It is hoped that this research may aid the prediction of the time elapsed since trauma in a forensic context and broaden the scope of trauma analysis in forensic anthropology

Spatiotemporal analysis of human actions using RGB-D cameras

Markerless human motion analysis has strong potential to provide cost-efficient solution for action recognition and body pose estimation. Many applications including humancomputer interaction, video surveillance, content-based video indexing, and automatic annotation among others will benefit from a robust solution to these problems. Depth sensing technologies in recent years have positively changed the climate of the automated vision-based human action recognition problem, deemed to be very difficult due to the various ambiguities inherent to conventional video. In this work, first a large set of invariant spatiotemporal features is extracted from skeleton joints (retrieved from depth sensor) in motion and evaluated as baseline performance. Next we introduce a discriminative Random Decision Forest-based feature selection framework capable of reaching impressive action recognition performance when combined with a linear SVM classifier. This approach improves upon the baseline performance obtained using the whole feature set with a significantly less number of features (one tenth of the original). The approach can also be used to provide insights on the spatiotemporal dynamics of human actions. A novel therapeutic action recognition dataset (WorkoutSU-10) is presented. We took advantage of this dataset as a benchmark in our tests to evaluate the reliability of our proposed methods. Recently the dataset has been published publically as a contribution to the action recognition community. In addition, an interactive action evaluation application is developed by utilizing the proposed methods to help with real life problems such as 'fall detection' in the elderly people or automated therapy program for patients with motor disabilities

USSR Space Life Sciences Digest, volume 1, no. 4

An overview of the developments and direction of the USSR Space Life Sciences Program is given. Highlights of launches, program development, and mission planning are given. Results of ground-based research and space flight studies are summarized. Topics covered include: space medicine and physiology; space biology, and life sciences and technology